Every tangible material is defined by its intrinsic properties, from the smallest simple atomic particle to the largest elementally complicated object. Spectroscopy reveals the intrinsic spectral properties by how a material responds to illuminating electromagnetic energy. The environment can alter intrinsic properties of a material therefore description of the environment needs to be a defining part of intrinsic properties. It is important to understand that intrinsic properties are not a function of the illumination. For example, the absorption envelope of a material is an intrinsic property of the material that will absorb only when the illumination wavelengths fall within that envelop. The intrinsic properties are independent and provide a consistency to the spectra, regardless of the illumination or instrumentation.
An intrinsic spectrum is defined as having only spectral components that are produced when a material absorbs electromagnetic energy. These intrinsic components include, but not limited to, absorption, emission, transmission and partial-reflection. Any illumination energy that is not absorbed is considered irrelevant and removed from the intrinsic spectrum.
U.S. Pat. Nos. 9,435,687, 9,998,636, and 10,337,916, incorporated herein by reference in their entirety, teach that irrelevant spectral components can be eliminated to reveal only the intrinsic spectral components of a material of interest. As explained in the above-referenced patents, in order to eliminate the irrelevant components, two types of spectra are required: 1) the target spectrum containing intrinsic and irrelevant components, and 2) the reference spectrum containing the illumination, foreground and background spectral components. By simply subtracting the reference spectrum from the target spectrum will not produce an intrinsic spectrum. The instrument must first be calibrated to obtain a spectrum that has zero intensity values across the wavelength range of the illumination. This is accomplished by first obtaining an empty target spectrum that is also devoid of any material components and an empty reference spectrum that is devoid of any material components. By subtracting the empty reference spectrum from the target spectrum, a Residual Spectrum is created representing the differences between the target and reference spectra across the illumination wavelength range. By adding the Residual Spectrum back into the reference spectrum and then subtracting this adjusted reference spectrum from the target spectrum, the resulting spectrum will have zero intensity across the illumination wavelength range. This spectrum is referred to as a Zero Order Spectrum and serves as validation that the instrument is calibrated. In subsequent acquisition of data at the same instrument settings, the Residual Spectrum from the calibration step is added to the reference spectrum of the new data and that adjusted spectrum is subtracted from the new target spectrum to form the Intrinsic Spectrum.
Imaging data cubes from mono-spectral, multi-spectral and hyper-spectral cameras contain two types of data; spatial and spectral, where each pixel in a spatial field of view is associated with the spectrum derived from the material imaged by that pixel. When the spectra are super imposed onto the spatial image, this results in a spectral image that indicates the location of the materials of interest within the spatial field of view.
When performing spectral imaging over long distances, for example, from high altitude drones or satellites, irrelevant spectral components may be present in the foreground (the atmosphere) of the field of view. These spectral components include, but are not limited to, particulate aerosol content, and organic gaseous pollution, water vapor and Rayleigh light scatter that can overwhelm and hide the intrinsic spectral components of the material in the field of view (FIG. 1). The spectral content and concentration of these irrelevant components are constantly varying in the atmosphere due to weather conditions and the position of the sun. Presently, mathematical models are employed to estimate these foreground components in an attempt to eliminate them from the final spectral image. However, even the best modeling cannot accurately estimate foreground components present at the exact time the data cube was obtained. Actual measurement of these irrelevant spectral components acquisition is required to accurately determine and eliminate them from the spectral image.
The strongest irrelevant component within the data cubes is the illumination spectrum upon which the intrinsic spectral and irrelevant spectral components appear. When the material of interest absorbs a portion of the illumination energy it generates intrinsic spectral components and the remainder of the illumination energy can be eliminated from the resulting intrinsic spectrum via the methodology described in this description and the above-mentioned patents. Thus, the only other constant component in the intrinsic spectrum is the random instrument noise from mechanical, electronic, and thermal sources, as discussed in U.S. Pat. No. 10,337,916, incorporated herein in its entirety by reference.
U.S. Pat. No. 9,998,636, incorporated herein in its entirety by reference, explains how the intrinsic spectra are obtained for each pixel associated with a spatial image of the field of view from two independent data cubes. Theoretically, the intrinsic spectra are obtained by taking separate reference and target data cubes at the same time but of different fields of view that have the same illumination and foreground characteristics.
The challenge of increasing the signal intensity from low intensity spectral components in an image has been addressed in spectroscopy and photography through data processing algorithms. With spectroscopy, especially within the infrared wavelength range, Fourier transform analysis has improved the detection of low intensity spectral components. In the field of astronomy, astro-imaging techniques involve obtaining multiple long exposure images and processing them by aligning fields of view of the exposures and stacking them in an additive manner.
Cameras have also gone through rapid technical development during the last 30 years, mostly due to the improvement of cooled CCD and CMOS cameras with 16-bit sensitivity and low dark field noise. The camera improvements coupled with advanced image processing of combining multiple long exposures have produced stunning astro-images, as well as, valuable scientific information with respect to the spatial and spectral properties of the celestial objects being imaged. One of the most important factors in obtaining such high-quality terrestrial-based astro-images is the degree of darkness of the sky. It is advantageous to obtain data using the most sensitive low noise cameras under the darkest skies.
Dark sky earth bound astro-imaging is seasonally limited since only specific constellations are in the night sky during specific times of the year. During daytime, imaging astronomical objects is usually limited to the sun, the moon, and a few planets of the solar system such as Venus and Mercury. For imaging other astronomical objects during daylight, powerful sophisticated radio telescopes are required.
Pixel saturation is the technical factor preventing astro-imaging during daylight. Most of the modern cameras are based on 16-bit data providing a well depth of only 65,536. For these cameras, intensities greater than 65,536 are no longer within the linear range where image processing can be mathematically accomplished. Sunlight can completely overwhelm and saturate the pixels in these 16 bit cameras. The use of neutral density filters, high F-stops and very short exposure times can bring sunlight into the linear range, but it will proportionately reduce the low intensity of the astronomical objects against the illumination of the solar spectrum leaving them undetectable.